bibtype J - Journal Article
ARLID 0456860
utime 20240103211936.3
mtime 20160316235959.9
title (primary) (eng) Measuring Information Loss in Managerial Decision
specification
page_count 7 s.
media_type P
serial
ARLID cav_un_epca*0456859
ISSN 2414-6498
title Academic Journal of Management Science Research
volume_id 1
volume 1 (2016)
page_num 26-32
keyword stochastic optimization
keyword Gini index
keyword newsvendor problem
keyword information loss
author (primary)
ARLID cav_un_auth*0101227
name1 Volf
name2 Petr
full_dept (cz) Stochastická informatika
full_dept (eng) Department of Stochastic Informatics
department (cz) SI
department (eng) SI
institution UTIA-B
full_dept Department of Stochastic Informatics
share 100
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
url http://library.utia.cas.cz/separaty/2016/SI/volf-0456860.pdf
cas_special
project
project_id GA13-14445S
agency GA ČR
ARLID cav_un_auth*0292652
abstract (eng) Traditional decision theory dealing with uncertainty is usually considering criteria based on expected values, or, variantly, on selected quantiles of objective function. Both, however, take into account just rather small part of available information, in particular not counting with possible variability of involved random variables. That is why the criteria based simultaneously on a set of reasonable characteristics should be preferred. This leads to a multiobjective problem and solution based on an appropriate utility function. In the present paper we propose quantitative characteristics measuring information loss caused by reduction of information used in decision. Such measures can help us to find a trade-off between the decision problem complexity and its reasonable simplified re-formulation. This concept is illustrated on examples.
reportyear 2017
RIV BB
num_of_auth 1
inst_support RVO:67985556
permalink http://hdl.handle.net/11104/0258404
confidential S
arlyear 2016
mrcbU63 cav_un_epca*0456859 Academic Journal of Management Science Research 2414-6498 Roč. 1 č. 1 2016 26 32